Cross Lingual Information Retrieval Using Search Engine and Data Mining
With the explosive growth of international users, distributed information and the number of linguistic resources, accessible throughout the World Wide Web, information retrieval has become crucial for users to find, retrieve and understand relevant information, in any language and form. Cross- Language Information Retrieval (CLIR) is a subfield of Information Retrieval which provides a query in one language and searches document collections in one or many languages but it also has a specific meaning of crosslanguage information retrieval where a document collection is multilingual. In the present research, we focus on query translation, disambiguation of multiple translation candidates and query expansion with various combinations, in order to improve the effectiveness of retrieval. Extracting, selecting and adding terms that emphasize query concepts are performed using expansion techniques such as, pseudo-relevance feedback, domain-based feedback and thesaurus-based expansion. A method for information retrieval for a query expressed in a native language is presented in this paper. It uses insights from data mining and intelligent search for formulating the query and parsing the results.